OPTIMIZATION OF THE PROCESS PARAMETERS OF D2STEEL ON EDM USING GREY RELATIONAL ANALYSIS

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 11, November 2018, pp , Article ID: IJMET_09_11_19 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed OPTIMIZATION OF THE PROCESS PARAMETERS OF D2STEEL ON EDM USING GREY RELATIONAL ANALYSIS Sunita Singh Naik* Assistant Professor, Dept. of Mechanical Engg.,VSSUT,Burla Prof.Dr Jaydev Rana Professor, Dept. of Mechanical Engg.,VSSUT,Burla, *corresponding author ABSTRACT Electro-discharge machining is one of the non-conventional machining processes. In this process both tool and workpiece are electrically conductive materials. The thermal energy is used to remove the material from the workpiece. Our main aim is to get best quality of the product without increasing the cost. So that optimization technique can be used to get the best optimal level for the machining parameters and that parameters enhance the performance characteristics of the product. Here we take the D2 steel as a workpiece and copper as an electrode. The machining parameters are input current, pulse on time, pulse of time, flushing pressure with their different levels. The output parameters are Material Removal Rate, Tool Wear Rate, Surface Roughness, Radial OVERCUT, Crack Width, and Surface Crack Density. Here orthogonal array is used. Grey relational analysis optimization method is used to find out the optimal level of the machining parameters. Then the Anova analysis can be obtained. Using the multiregression analysis the optimum value can be calculated and that is compared with the experimental value. Key words: EDM, Grey relational analysis, ANOVA, orthogonal array Cite this Article: Sunita Singh Naik and Prof.Dr Jaydev Rana, Optimization of the Process Parameters of D2steel on Edm Using Grey Relational Analysis, International Journal of Mechanical Engineering and Technology, 9(11), 2018, pp INTRODUCTION Electro-discharge machining is an advanced machining process. The tool material is not harder than workpiece material. In this process the tool and workpiece are not contact to each other. The electrodes are electrically conductive materials and immersed in a dielectric fluid. A series of rapid, repetitive and randomly distributed electric sparks occur within a constant spark gap between the tool and workpiece. These sparks cause the ionization of a dielectric medium at a critical voltage and establish an ionized channel called the plasma channel, which acts as the heat editor@iaeme.com

2 Optimization of the Process Parameters of D2steel on Edm Using Grey Relational Analysis source causing melting and vaporization of the workpiece. Various harder materials like hastalloy, nitralloy, nimonics, inconel, stainless steel, ceramics, titanium, high temperature resistant alloy, fibre reinforced alloy are machined in this process. Also Making of holes (noncircular, micro sized, large aspect ratio contoured holes without burrs) are difficult to achieve for the industries like aerospace, nuclear reactor, missiles, turbines, automobiles. For this reason nonconventional machining process is used. D2 steel is a high carbon high chromium tool steel and it is a very hard materials due to the presence of the alloyed elements of V, Cr and Mo. So that it is possible only EDM process. The D2 tool steel has several applications such as forming dies, extrusion dies and thread rolling. [1] R Prasad prathipati and etc. [1] state that using multiple whole electrodes rather than solid electrode in a AISI D2 steel, the machining time is reduced. Sanjay kumar Majhi and etc.[2] told that, pulse current and pulse on time increases the specific energy and MRR. Pulse current affects the surface roughness to increase. Pulse off current causes the reduction of tool wear and surface roughness. Y.H.Guu and etc [3] stated that the thickness of the recast layer and surface roughness are proportional to the power input. The EDM process introduces tensile residual stress on the machined surface. Praveen kumar singh and etc [4] told that using the copper and brass electrode on D2 steel, the copper gives better MRR than brass by increasing the current and when we change the gap voltage for both copper and brass, MRR will decrease. Raman K and etc [5] concluded that the relation of the machining parameters and the machinability factor when machining tool steel using EDM. It was concluded that the best performance was given by electrode with the diameter of 20mm at a current setting.5 amp having highest MRR and lowest TWR. 2. EXPERIMENTATION For this Experiment, the entire work was done in Electric Discharge Machine (model ECOWIN MIC-432CS CNC EDM) with servo-head (consistent crevice) and positive extremity for anode. Business grade EDM oil (particular gravity= 0.3, solidifying point= 94 C) was utilized as dielectric liquid. Here workpiece-d2 Tool Steel and COPPER Electrode of 15 mm diameter used in the EDM process. The input parameters and their levels are shown in Table-1. The experiments were designed and conducted as a orthogonal array and the output parameters are shown as Table-2. Here the output parameters are MRR-Material removal rate, TWR-tool wear rate, SRsurface roughness, ROC-radial overcut, CW-crack width, SCD-surface crack density. Machining parameters Table-1 Level-1 Level-2 Level-3 Input current Pulse on time Pulse off time Flushing pressure Table-2: editor@iaeme.com

3 Sunita Singh Naik and Prof.Dr Jaydev Rana Sl no Input Curre nt Amp) ( µs) ( µs) FP(kgf/c m2) MRR mm3/min TWR mm3/min SR µm %R OC C.W µm SCD µm GREY RELATIONAL ANALYSIS METHODOLOGY The Grey relational Analysis is a multi-response optimization technique and the optimal level of the machining parameters can be determined by this method using the grey relational grade. Step-1: In Grey Relational Analysis, the raw data was normalized first and it is placed in between zero to one. This process is known as Grey relational generation. The normalized of the data can be obtained by the formulas are Higher the better formula: = (1) Lower the better formula: = Where, = given sequence and the data pre-processing values are shown in table-3 Step-2: Deviation Sequence: The deviation for the reference and comparability sequence were found out by the formula (k) = (3) Where, denotes the comparability sequence. is generally taken as 1. Step-3: Grey relational coefficient: The Grey relational coefficient is calculated to express the relationship between the ideal (best) and actual normalized experimental results. The grey relational coefficient can be expressed as = (4) Where = deviation sequence of the reference sequence and comparability sequence. ξ = distinguishing or identified coefficient. The value of ξ is smaller and the distinguished ability is the larger, ξ=0.5 is generally used. Table-4 shows the GRC. (2) editor@iaeme.com

4 Optimization of the Process Parameters of D2steel on Edm Using Grey Relational Analysis Step-4: Grey relational grade was determined by averaging the grey relational coefficient corresponding to each performance characteristic. The overall performance characteristic of the multiple response process depends on the calculated grey relational grade. The grey relational grade can be expressed as = " Where, =grey relational grade for the $ %& '(')$*'+,, k = number of performance characteristics, n = number of process responses. The grey relational grade and the coefficient are presented in table-4 The higher value of grey relational grade represents the corresponding experimental result is closer to the ideally normalized value 1. The figure 1 shows the experimental number with GRG. Step-5: The experimental design is orthogonal; it is then possible to separate out the effect of each machining parameter on the GRG at different levels. The total mean of GRG is calculated. It is shown in table-5. Here figure-2 shows the level with the average response of GRG. Table-3: Data Preprocessing Sl no HTB-MRR STB-TWR STB-SR STB-%ROC STB-CW STB-SCD Sl no GRC- MRR GRC- TWR Table-4: Grey Relational Coefficient & Grade: GRC-SR GRC- %ROC GRC-CW GRC-SCD GRG RANK (5) editor@iaeme.com

5 Sunita Singh Naik and Prof.Dr Jaydev Rana GRG G r e y r e l a t i o n a l g r a d e Experimental Number GRG Figure-1: Experimental With Grey Relational Grade: Table-5 Average Response Table For GRG: LEVEL-1 LEVEL-2 LEVEL-3 MAX-MIN RANK A B C D OPTIMUM LEVEL-A3B3C1D1 0.0 B 0.5 AVG RESPONSE OF GRG A1 A2 A3 -- B1 B2 B3 -- C1 C2 C3 -- D1 D2 D3 LEVEL Figure-2: Level vs Average response of GRG: The Analysis of variance can be evaluated to find the significant machining parameters and it is shown in table-. Then the confirmation test can be obtained. The estimated GRG can be calculated using the equation editor@iaeme.com

6 Optimization of the Process Parameters of D2steel on Edm Using Grey Relational Analysis Source of variance Machining parameter Table-: Anova DOF SS MS FO % A A B B C C D D ERROR ERROR ERROR 0 0 TOTAL TOTAL Confirmation test γ=γm+(γi-γm) () The estimated GRG value is and it matches with A3B3C2D TABLE-: The value of optimum and initial value. Optimum experiment A3B3C1D INITIAL experiment A3B3C2D MRR TWR SR OVERCUT CW SCD CONCLUSION From this study, we get the optimum level for the machining parameters that is A3B3C1D1. The estimated Grey relational grade is Using the multi regression analysis the value of Material Removal Rate,Tool Wear Rate, Surface Roughness, Radial Overcut, CrackWidth, SurfaceCrack Density are , 0.215, 3.8, 0.45, , respectively. This is compared and shown in a table- REFERENCES [1] R Prasad prathipati, venkateswaralu devuru, muralimahan cheepu, kondaiah gudimetla and ruzwal kiran, machining of AISI D2 tool steel with multiple hole electrodes by EDM process, IOP conference series:materials science and engineering, volume 330, conference 1. [2] sanjay kumar majhi, T.K.Mishra, M.K.Pradhan and Hargovind soni, Effect of machining parameters of AISI D2 tool steel on EDM, International journal of current Engineering and technology. [3] Y.H.Guu, C.S.Deng, Effect of EDM on surface characteristics and machining damage of AISI D2 tool steel, materials science and Engineering A. [4] Praveen kumar singh, Dinesh kumar Rao, Anshika Gupta, Experimental studies for mrr on AISI D2 steel using EDM, international journal on emerging technology. [5] Raman k, Sathiya G.K, saisujith k,mani p, Effect of machining parameters in Electric discharge machining of D2 tool steel, international journal of science and research editor@iaeme.com